Time to compare cost of living around the world.

Our dataset will be used from website https://www.numbeo.com/cost-of-living/

Let's read collected data

In [4]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import folium
import geopandas as gpd
from IPython.display import display, HTML, IFrame
import requests
In [2]:
file = r'C:\Users\rocze\Desktop\pythonProject\COL.json'
In [3]:
data = pd.read_json(file)
print(data)

        CountryName Meal, Inexpensive Restaurant  \
0       Afghanistan                       1.67 $   
1     Aland Islands                      13.09 $   
2           Albania                       5.51 $   
3          Alderney                      22.32 $   
4           Algeria                       2.19 $   
..              ...                          ...   
229         Vietnam                       2.12 $   
230  Western Sahara                       3.20 $   
231           Yemen                       5.99 $   
232          Zambia                       3.67 $   
233        Zimbabwe                       6.00 $   

    Meal for 2 People, Mid-range Restaurant, Three-course  \
0                                               7.78 $      
1                                              64.10 $      
2                                              27.57 $      
3                                              72.38 $      
4                                              14.63 $      
..                                                 ...      
229                                            21.19 $      
230                                            26.71 $      
231                                            19.97 $      
232                                            29.40 $      
233                                            40.00 $      

    McMeal at McDonalds (or Equivalent Combo Meal) Cappuccino (regular)  \
0                                           3.08 $               1.06 $   
1                                          10.68 $               3.74 $   
2                                           5.51 $               1.31 $   
3                                           9.65 $               3.52 $   
4                                           3.66 $               0.61 $   
..                                             ...                  ...   
229                                         4.66 $               1.84 $   
230                                         5.34 $               1.07 $   
231                                         9.99 $               3.18 $   
232                                         3.41 $               1.75 $   
233                                         6.00 $               2.21 $   

    Coke/Pepsi (0.33 liter bottle) Water (0.33 liter bottle)  \
0                           0.35 $                    0.16 $   
1                           2.24 $                    1.74 $   
2                           1.32 $                    0.63 $   
3                           1.99 $                    1.01 $   
4                           0.48 $                    0.20 $   
..                             ...                       ...   
229                         0.53 $                    0.32 $   
230                         0.53 $                    0.32 $   
231                         1.37 $                    0.67 $   
232                         0.83 $                    0.46 $   
233                         0.65 $                    0.47 $   

    Milk (regular), (1 liter) Loaf of Fresh White Bread (500g)  \
0                      0.58 $                           0.23 $   
1                      1.10 $                           1.87 $   
2                      1.35 $                           0.72 $   
3                      1.81 $                           2.41 $   
4                      0.78 $                           0.16 $   
..                        ...                              ...   
229                    1.48 $                           1.02 $   
230                    0.75 $                           0.53 $   
231                    2.26 $                           1.25 $   
232                    1.19 $                           0.98 $   
233                    1.58 $                           1.00 $   

    Rice (white), (1kg)  ... 1 Pair of Nike Running Shoes (Mid-Range)  \
0                1.20 $  ...                                  29.64 $   
1                2.07 $  ...                                 117.51 $   
2                1.42 $  ...                                  74.80 $   
3                     ?  ...                                        ?   
4                1.09 $  ...                                  65.82 $   
..                  ...  ...                                      ...   
229              0.97 $  ...                                  93.32 $   
230              1.71 $  ...                                  69.44 $   
231              4.00 $  ...                                  74.23 $   
232              1.83 $  ...                                  90.18 $   
233              1.49 $  ...                                  39.44 $   

    1 Pair of Men Leather Business Shoes Apartment (1 bedroom) in City Centre  \
0                                34.27 $                              93.89 $   
1                               131.76 $                             747.82 $   
2                                86.88 $                             353.76 $   
3                                      ?                                    ?   
4                                75.38 $                             167.91 $   
..                                   ...                                  ...   
229                              93.98 $                             377.45 $   
230                              74.78 $                             320.50 $   
231                              89.56 $                             265.13 $   
232                             105.64 $                             274.41 $   
233                              41.11 $                             282.35 $   

    Apartment (1 bedroom) Outside of Centre  \
0                                   55.57 $   
1                                  560.87 $   
2                                  221.69 $   
3                                         ?   
4                                  106.18 $   
..                                      ...   
229                                235.36 $   
230                                160.25 $   
231                                163.62 $   
232                                193.01 $   
233                                260.83 $   

    Apartment (3 bedrooms) in City Centre  \
0                                226.32 $   
1                              1,228.57 $   
2                                610.71 $   
3                                       ?   
4                                282.35 $   
..                                    ...   
229                              859.01 $   
230                              640.99 $   
231                              540.18 $   
232                              924.31 $   
233                              828.57 $   

    Apartment (3 bedrooms) Outside of Centre  \
0                                   137.82 $   
1                                   988.19 $   
2                                   363.63 $   
3                                   965.10 $   
4                                   185.38 $   
..                                       ...   
229                                 492.50 $   
230                                 267.08 $   
231                                 403.36 $   
232                                 438.29 $   
233                                 661.54 $   

    Price per Square Meter to Buy Apartment in City Centre  \
0                                             614.26 $       
1                                           2,848.85 $       
2                                           1,747.62 $       
3                                                    ?       
4                                           1,047.51 $       
..                                                 ...       
229                                         2,754.98 $       
230                                           598.26 $       
231                                         1,304.22 $       
232                                        10,763.91 $       
233                                         1,069.10 $       

    Price per Square Meter to Buy Apartment Outside of Centre  \
0                                             251.91 $          
1                                           2,029.80 $          
2                                             900.76 $          
3                                                    ?          
4                                             626.80 $          
..                                                 ...          
229                                         1,465.91 $          
230                                           491.43 $          
231                                           789.14 $          
232                                        39,211.94 $          
233                                         1,266.67 $          

    Average Monthly Net Salary (After Tax)  \
0                                 181.99 $   
1                               2,599.57 $   
2                                 420.96 $   
3                                        ?   
4                                 263.73 $   
..                                     ...   
229                               445.56 $   
230                               213.66 $   
231                               378.65 $   
232                               396.94 $   
233                               295.45 $   

    Mortgage Interest Rate in Percentages (%), Yearly, for 20 Years Fixed-Rate  
0                                                13.36                          
1                                                 1.95                          
2                                                 4.73                          
3                                                    ?                          
4                                                 6.57                          
..                                                 ...                          
229                                               9.98                          
230                                                  ?                          
231                                              11.25                          
232                                              23.25                          
233                                              15.00                          

[234 rows x 50 columns]

That's a lot of data, we should focus of few columns.

In [5]:
selectedData = data[['CountryName','Coke/Pepsi (0.33 liter bottle)','Water (0.33 liter bottle)','Rice (white), (1kg)','1 Pair of Nike Running Shoes (Mid-Range)','Apartment (1 bedroom) in City Centre','Apartment (3 bedrooms) in City Centre','Price per Square Meter to Buy Apartment in City Centre','Average Monthly Net Salary (After Tax)']]
print(selectedData)

        CountryName Coke/Pepsi (0.33 liter bottle) Water (0.33 liter bottle)  \
0       Afghanistan                         0.35 $                    0.16 $   
1     Aland Islands                         2.24 $                    1.74 $   
2           Albania                         1.32 $                    0.63 $   
3          Alderney                         1.99 $                    1.01 $   
4           Algeria                         0.48 $                    0.20 $   
..              ...                            ...                       ...   
229         Vietnam                         0.53 $                    0.32 $   
230  Western Sahara                         0.53 $                    0.32 $   
231           Yemen                         1.37 $                    0.67 $   
232          Zambia                         0.83 $                    0.46 $   
233        Zimbabwe                         0.65 $                    0.47 $   

    Rice (white), (1kg) 1 Pair of Nike Running Shoes (Mid-Range)  \
0                1.20 $                                  29.64 $   
1                2.07 $                                 117.51 $   
2                1.42 $                                  74.80 $   
3                     ?                                        ?   
4                1.09 $                                  65.82 $   
..                  ...                                      ...   
229              0.97 $                                  93.32 $   
230              1.71 $                                  69.44 $   
231              4.00 $                                  74.23 $   
232              1.83 $                                  90.18 $   
233              1.49 $                                  39.44 $   

    Apartment (1 bedroom) in City Centre  \
0                                93.89 $   
1                               747.82 $   
2                               353.76 $   
3                                      ?   
4                               167.91 $   
..                                   ...   
229                             377.45 $   
230                             320.50 $   
231                             265.13 $   
232                             274.41 $   
233                             282.35 $   

    Apartment (3 bedrooms) in City Centre  \
0                                226.32 $   
1                              1,228.57 $   
2                                610.71 $   
3                                       ?   
4                                282.35 $   
..                                    ...   
229                              859.01 $   
230                              640.99 $   
231                              540.18 $   
232                              924.31 $   
233                              828.57 $   

    Price per Square Meter to Buy Apartment in City Centre  \
0                                             614.26 $       
1                                           2,848.85 $       
2                                           1,747.62 $       
3                                                    ?       
4                                           1,047.51 $       
..                                                 ...       
229                                         2,754.98 $       
230                                           598.26 $       
231                                         1,304.22 $       
232                                        10,763.91 $       
233                                         1,069.10 $       

    Average Monthly Net Salary (After Tax)  
0                                 181.99 $  
1                               2,599.57 $  
2                                 420.96 $  
3                                        ?  
4                                 263.73 $  
..                                     ...  
229                               445.56 $  
230                               213.66 $  
231                               378.65 $  
232                               396.94 $  
233                               295.45 $  

[234 rows x 9 columns]

But let's first clear our data, we have to remove some things from our table

In [6]:
selectedData = selectedData.applymap(lambda x: x.replace('\u00a0$', '') if isinstance(x, str) else x)
selectedData = selectedData.applymap(lambda x: x.replace(',', '') if isinstance(x, str) else x)
selectedData.iloc[:, 1:] = selectedData.iloc[:, 1:].apply(lambda x: pd.to_numeric(x, errors='coerce'))
print(selectedData)

        CountryName Coke/Pepsi (0.33 liter bottle) Water (0.33 liter bottle)  \
0       Afghanistan                           0.35                      0.16   
1     Aland Islands                           2.24                      1.74   
2           Albania                           1.32                      0.63   
3          Alderney                           1.99                      1.01   
4           Algeria                           0.48                       0.2   
..              ...                            ...                       ...   
229         Vietnam                           0.53                      0.32   
230  Western Sahara                           0.53                      0.32   
231           Yemen                           1.37                      0.67   
232          Zambia                           0.83                      0.46   
233        Zimbabwe                           0.65                      0.47   

    Rice (white), (1kg) 1 Pair of Nike Running Shoes (Mid-Range)  \
0                   1.2                                    29.64   
1                  2.07                                   117.51   
2                  1.42                                     74.8   
3                   NaN                                      NaN   
4                  1.09                                    65.82   
..                  ...                                      ...   
229                0.97                                    93.32   
230                1.71                                    69.44   
231                 4.0                                    74.23   
232                1.83                                    90.18   
233                1.49                                    39.44   

    Apartment (1 bedroom) in City Centre  \
0                                  93.89   
1                                 747.82   
2                                 353.76   
3                                    NaN   
4                                 167.91   
..                                   ...   
229                               377.45   
230                                320.5   
231                               265.13   
232                               274.41   
233                               282.35   

    Apartment (3 bedrooms) in City Centre  \
0                                  226.32   
1                                 1228.57   
2                                  610.71   
3                                     NaN   
4                                  282.35   
..                                    ...   
229                                859.01   
230                                640.99   
231                                540.18   
232                                924.31   
233                                828.57   

    Price per Square Meter to Buy Apartment in City Centre  \
0                                               614.26       
1                                              2848.85       
2                                              1747.62       
3                                                  NaN       
4                                              1047.51       
..                                                 ...       
229                                            2754.98       
230                                             598.26       
231                                            1304.22       
232                                           10763.91       
233                                             1069.1       

    Average Monthly Net Salary (After Tax)  
0                                   181.99  
1                                  2599.57  
2                                   420.96  
3                                      NaN  
4                                   263.73  
..                                     ...  
229                                 445.56  
230                                 213.66  
231                                 378.65  
232                                 396.94  
233                                 295.45  

[234 rows x 9 columns]

Therefore we need to change the type of columns from object to numerical

In [7]:
selectedData['Coke/Pepsi (0.33 liter bottle)'] = selectedData['Coke/Pepsi (0.33 liter bottle)'].astype(float)
selectedData['Water (0.33 liter bottle)'] = selectedData['Water (0.33 liter bottle)'].astype(float)
selectedData['Rice (white), (1kg)'] = selectedData['Rice (white), (1kg)'].astype(float)
selectedData['1 Pair of Nike Running Shoes (Mid-Range)'] = selectedData['1 Pair of Nike Running Shoes (Mid-Range)'].astype(float)
selectedData['Apartment (1 bedroom) in City Centre'] = selectedData['Apartment (1 bedroom) in City Centre'].astype(float)
selectedData['Apartment (3 bedrooms) in City Centre'] = selectedData['Apartment (3 bedrooms) in City Centre'].astype(float)
selectedData['Price per Square Meter to Buy Apartment in City Centre'] = selectedData['Price per Square Meter to Buy Apartment in City Centre'].astype(float)
selectedData['Average Monthly Net Salary (After Tax)'] = selectedData['Average Monthly Net Salary (After Tax)'].astype(float)
selectedData.dtypes
Out[7]:
CountryName                                                object
Coke/Pepsi (0.33 liter bottle)                            float64
Water (0.33 liter bottle)                                 float64
Rice (white), (1kg)                                       float64
1 Pair of Nike Running Shoes (Mid-Range)                  float64
Apartment (1 bedroom) in City Centre                      float64
Apartment (3 bedrooms) in City Centre                     float64
Price per Square Meter to Buy Apartment in City Centre    float64
Average Monthly Net Salary (After Tax)                    float64
dtype: object

Our columns names are little to long.

In [8]:
selectedData.columns=['Country','Coke 0.33l','Water 0.33l','Rice 1kg','Nike Runners','1B Apartment','3B Apartment','1SM Price in Center','Avg Salary']
print(selectedData)

            Country  Coke 0.33l  Water 0.33l  Rice 1kg  Nike Runners  \
0       Afghanistan        0.35         0.16      1.20         29.64   
1     Aland Islands        2.24         1.74      2.07        117.51   
2           Albania        1.32         0.63      1.42         74.80   
3          Alderney        1.99         1.01       NaN           NaN   
4           Algeria        0.48         0.20      1.09         65.82   
..              ...         ...          ...       ...           ...   
229         Vietnam        0.53         0.32      0.97         93.32   
230  Western Sahara        0.53         0.32      1.71         69.44   
231           Yemen        1.37         0.67      4.00         74.23   
232          Zambia        0.83         0.46      1.83         90.18   
233        Zimbabwe        0.65         0.47      1.49         39.44   

     1B Apartment  3B Apartment  1SM Price in Center  Avg Salary  
0           93.89        226.32               614.26      181.99  
1          747.82       1228.57              2848.85     2599.57  
2          353.76        610.71              1747.62      420.96  
3             NaN           NaN                  NaN         NaN  
4          167.91        282.35              1047.51      263.73  
..            ...           ...                  ...         ...  
229        377.45        859.01              2754.98      445.56  
230        320.50        640.99               598.26      213.66  
231        265.13        540.18              1304.22      378.65  
232        274.41        924.31             10763.91      396.94  
233        282.35        828.57              1069.10      295.45  

[234 rows x 9 columns]

Something wrong is with USA and other names, let's fix it!

In [10]:
row = selectedData.loc[selectedData['Country'] == "United States"]
selectedData.at[row.index[0], 'Country'] = 'United States of America'
row2 = selectedData.loc[selectedData['Country'] == "Czech Republic"]
selectedData.at[row2.index[0], 'Country'] = 'Czechia'
row3 = selectedData.loc[selectedData['Country'] == "Kosovo (Disputed Territory)"]
selectedData.at[row3.index[0], 'Country'] = 'Kosovo'
row4 = selectedData.loc[selectedData['Country'] == "Republic Of Congo"]
selectedData.at[row4.index[0], 'Country'] = 'Dem. Rep. Congo'
row5 = selectedData.loc[selectedData['Country'] == "South Sudan"]
selectedData.at[row5.index[0], 'Country'] = 'S. Sudan'
row6 = selectedData.loc[selectedData['Country'] == "Bosnia And Herzegovina"]
selectedData.at[row6.index[0], 'Country'] = 'Bosnia and Herz.'

For our visualization we need world map

In [11]:
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
world.to_file("world-countries.json", driver="GeoJSON")
In [12]:
map=r"C:\Users\rocze\world-countries.json"
m = folium.Map(location=[48, -102], zoom_start=3)
folium.Choropleth(
    geo_data=map,
    data=selectedData,
    columns=["Country", "Avg Salary"],
    key_on="feature.properties.name",
    fill_color="YlGn",
    fill_opacity=0.7,
    line_opacity=0.2,
    legend_name="Average Salary (USD)",
    reset=True
).add_to(m)
m.save("avg_salary_map.html")

Here is the map we created: https://github.com/Alcosmao/CostOfLifeAroundTheWorld/blob/main/avg_salary_map.html

But let's create also non interactiv map

In [13]:
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
world = world[['name', 'geometry']]
merged = world.merge(selectedData, left_on='name', right_on='Country', how='left')
merged['range'] = pd.cut(merged['Avg Salary'], bins=11)
fig, ax = plt.subplots(figsize=(14, 10))
merged.plot(column='range', cmap='Greens', linewidth=0.8, ax=ax, edgecolor='0.8', legend=True,legend_kwds={"loc": "lower left", "title": "Average Salary in USD p/m", "frameon": False})
ax.set_title('Average $ monthly salary by Country')
ax.set_axis_off()
plt.show()

Time to show top 5 bottom and top countries

In [14]:
selectedData = selectedData.dropna(subset=['Avg Salary'])
sortedData = selectedData.sort_values(by='Avg Salary')
topFive = sortedData.tail(5)
bottomFive = sortedData.head(5)
concatData = pd.concat([topFive, bottomFive])
ax = concatData.plot(kind='bar', x='Country', y='Avg Salary', color=['red', 'blue', 'green', 'orange', 'purple', 'purple', 'orange', 'green', 'blue', 'red'])
for x in ax.containers:
    ax.bar_label(x, label_type='edge')
plt.show()

We can hardly see the bottom values. Try another way

In [15]:
topFive = sortedData.tail(5)
bottomFive = sortedData.head(5)

plt.bar(topFive["Country"], topFive["Avg Salary"], color=['red', 'blue', 'green', 'orange', 'purple'])
for i, v in enumerate(topFive["Avg Salary"]):
    plt.text(i, v, str(v), ha='center', va='bottom')
plt.title("Top 5 Countries by Average Salary")
plt.xlabel("Country")
plt.ylabel("Average Salary")

plt.figure()
plt.bar(bottomFive["Country"], bottomFive["Avg Salary"], color=['red', 'blue', 'green', 'orange', 'purple'])
for i, v in enumerate(bottomFive["Avg Salary"]):
    plt.text(i, v, str(v), ha='center', va='bottom')
plt.title("Bottom 5 Countries by Average Salary")
plt.xlabel("Country")
plt.ylabel("Average Salary")


plt.show()

Okay, time to see how much of the kg rice we can by for our salary in each country

In [16]:
selectedData = selectedData.dropna(subset=['Rice 1kg', 'Avg Salary'])
selectedData['Rice per Salary'] = selectedData['Avg Salary'] / selectedData['Rice 1kg']
plt.scatter(selectedData['Avg Salary'], selectedData['Rice per Salary'])
plt.xlabel('Average Salary')
plt.ylabel('Rice per Salary')
plt.show()

I don't think we can see anything here! Let's do it more interactive!

In [17]:
fig = px.scatter(selectedData, x="Rice per Salary", y="Avg Salary", color="Country", hover_name="Country", size_max=70)
fig.update_layout(
    title="Rice Affordability by Country",
    xaxis_title="Rice per Salary (Kg)",
    yaxis_title="Average Salary per Month (USD)",
    legend_title="Country",
    hoverlabel=dict(
        bgcolor="black",
        font_size=16,
        font_family="Arial"
    )
)
fig.show()
0100020003000400050000100020003000400050006000
CountryAfghanistanAland IslandsAlbaniaAlgeriaAndorraAngolaAnguillaAntigua And BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBoliviaBosnia and Herz.BotswanaBrazilBritish Virgin IslandsBruneiBulgariaBurkina FasoBurundiCambodiaCameroonCanadaCape VerdeCayman IslandsCentral African RepublicChadChileChinaColombiaCongoCook IslandsCosta RicaCroatiaCubaCuracaoCyprusCzechiaDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuernseyGuineaGuinea-BissauGuyanaHaitiHondurasHong Kong (China)HungaryIcelandIndiaIndonesiaIranIraqIrelandIsle Of ManIsraelItalyIvory CoastJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKosovoKuwaitKyrgyzstanLaosLatviaLebanonLesothoLiberiaLibyaLiechtensteinLithuaniaLuxembourgMacao (China)MadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMexicoMoldovaMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNepalNetherlandsNew CaledoniaNew ZealandNicaraguaNigerNigeriaNorfolk IslandNorth MacedoniaNorwayOmanPakistanPalestinePanamaPapua New GuineaParaguayPeruPhilippinesPolandPortugalPuerto RicoQatarDem. Rep. CongoReunionRomaniaRussiaRwandaSaint HelenaSaint LuciaSaint Vincent And The GrenadinesSamoaSan MarinoSao Tome And PrincipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSint MaartenSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth KoreaS. SudanSpainSri LankaSudanSurinameSwazilandSwedenSwitzerlandSyriaTaiwan (China)TajikistanTanzaniaThailandTimor-LesteTogoTongaTrinidad And TobagoTunisiaTurkeyTurkmenistanTurks And Caicos IslandsTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited States of AmericaUruguayUs Virgin IslandsUzbekistanVanuatuVenezuelaVietnamWestern SaharaYemenZambiaZimbabweRice Affordability by CountryRice per Salary (Kg)Average Salary per Month (USD)
plotly-logomark

When You click on country from list or dot on chart all information will be displayed!

For the next chart, let's calculate how many years it would take to save enough money for a 72m2 flat, assuming that we can save 20% of our salary every month.

In [18]:
selectedData['YearsNeededFor72mFlat'] = np.ceil(0.2 * (72 * selectedData['1SM Price in Center']) / selectedData['Avg Salary'])
print(selectedData[['Country','YearsNeededFor72mFlat']])

            Country  YearsNeededFor72mFlat
0       Afghanistan                   49.0
1     Aland Islands                   16.0
2           Albania                   60.0
4           Algeria                   58.0
6           Andorra                   24.0
..              ...                    ...
229         Vietnam                   90.0
230  Western Sahara                   41.0
231           Yemen                   50.0
232          Zambia                  391.0
233        Zimbabwe                   53.0

[222 rows x 2 columns]

We will skip values higher than 60 years, because it's not possible then to person to live that long to buy a flat!

In [19]:
selectedData = selectedData[selectedData["YearsNeededFor72mFlat"] < 60]
fig = px.choropleth(selectedData, locations="Country", locationmode="country names",
                    color="YearsNeededFor72mFlat",
                    hover_name="Country",
                    color_continuous_scale=px.colors.sequential.Plasma,
                    range_color=(selectedData["YearsNeededFor72mFlat"].min(), selectedData["YearsNeededFor72mFlat"].max()),
                    labels={"YearsNeededFor72mFlat": "Needed Years"})
fig.update_layout(title="Map of Needed Years to Save for 72 m² Flat", title_x=0.5)
fig.show()
01020304050Needed YearsMap of Needed Years to Save for 72 m² Flat
plotly-logomark

WORK IN PROGRESS

In [ ]: